3 papers
cs.LG2026
Distribution Alignment for One-Shot Federated Learning via Optimal Transport
Daniele Berardini, Vito Paolo Pastore, Vittorio Murino
One-Shot Federated Learning (OSFL) addresses extreme communication regimes in which clients interact with the server only once, amplifying the impact of heterogeneous client data d…
physics.flu-dyn2026
Physics-Constrained Neural Closure for Lattice Boltzmann Large-Eddy Simulation
Muhammad Idrees Khan, Sauro Succi, Hua-Dong Yao +1
We present a physics-constrained, data-driven subgrid-scale (SGS) stress closure for large-eddy simulation (LES) in the lattice Boltzmann method (LBM). Trained on filtered-downsamp…
physics.flu-dyn2025
Validating the Boltzmann approach to the Large-Eddy simulations of forced homogeneous incompressible turbulence
Muhammad Idrees Khan, Sauro Succi, Giacomo Falcucci
The simulation of turbulent flows remains a central challenge, as even our most powerful computers cannot resolve the finest scales of motion in many flows of practical interest. A…